(2,2-Diphenyl-[1,3]oxathiolan-5-ylmethyl)-(3-phenyl-propyl)-amine: a Potent and Selective 5-HT1AReceptor Agonist

Spiroxatrine derivatives towards 5-HT1A receptor selectivity

BACKGROUND

In our previous work, spiroxatrine was taken as reference compound to develop selective NOP ligands. Therefore, several triazaspirodecanone derivatives were synthesized. Here, we verify their selectivity towards other 5-HT₁ receptor subtypes and with respect to α₂-AR (Adrenergic Receptors).

METHODS

Binding affinities were determined on cells expressing human cloned receptors for 5-HT_1A/B/D and α_2A/B/C subtypes. The Ki values were determined for those with at least 50% radioligand inhibition.

RESULTS

All our derivatives show a moderate affinity for α₂ subtypes, spanning from 5 to 7.5 pK_i values. Moreover, they show affinity values in a μM-nM range at the 5-HT_1A receptor, while they are practically inactive at 5-HT_1B and 5-HT_1D subtypes. Compound 11, the best of the series, has a 5-HT_1A pK_i value of 8.43 similar to spiroxatrine but, notably, it has a 5-HT_1A favorable selectivity ratio of 52, 8 and 29, respectively over α_2A, α_2B and α_2C adrenoceptor subtypes.

CONCLUSIONS

In this SAR study, a 5-HT_1A selective ligand has been identified in which a tetralone moiety replaced the 1,4-benzodioxane of spiroxatrine and the methylene linker to the triazaspirodecanone portion was maintained in position 2.

1,3-Dioxane as a scaffold for potent and selective 5-HT1AR agonist with in-vivo anxiolytic, anti-depressant and anti-nociceptive activity

A series of compounds generated by ring expansion/opening and molecular elongation/simplification of the 1,3-dioxolane scaffold were prepared and tested for binding affinity at 5-HT_1AR and α₁ adrenoceptors. The compounds with greater affinity were selected for further functional studies. N-((2,2-diphenyl-1,3-dioxan-5-yl)methyl)-2-(2-methoxyphenoxy)ethan-1-ammonium hydrogen oxalate (12) emerged as highly potent full agonist at the 5-HT_1AR (pKi 5-HT_1A = 8.8; pD₂ = 9.22, %E_max = 92). The pharmacokinetic data in rats showed that the orally administered 12 has a high biodistribution in the brain compartment. Thus, 12 was further investigated in-vivo, showing an anxiolytic and antidepressant effect. Moreover, in the formalin test, 12 was able to decrease the late response to the noxious stimulus, indicating a potential use in the treatment of chronic pain.

Exhaustive CoMFA and CoMSIA analyses around different chemical entities: a ligand-based study exploring the affinity and selectivity profiles of 5-HT1A ligands

The 5-hydroxytryptamine (5-HT_1A) receptors represent an attractive target in drug discovery. In particular, 5-HT_1A agonists and partial agonists are deeply investigated for their potential role in the treatment of anxiety, depression, ischaemic brain disorder and more recently, of pain. On the other hand, 5-HT_1A antagonists have been revealed promising compounds in cognition disorders and, lately, in cancer. Thus, the discovery of 5HT_1A ligands is nowadays an appealing research activity in medicinal chemistry. In this work, Comparative Molecular Fields Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) were applied on an in-house library of 5-HT_1A ligands bearing different chemical scaffolds in order to elucidate their affinity and selectivity for the target_. Following this procedure, a number of structural modifications have been drawn for the development of much more effective 5-HT_1AR ligands.

A homology modelling-driven study leading to the discovery of the first mouse trace amine-associated receptor 5 (TAAR5) antagonists

The computational study here proposed allowed us to discovery for the first time two TAAR5 antagonist, selective over the TAAR1 receptor.

Enantiomeric resolution of [(2,2-diphenyl-1,3-dioxolan-4-yl)methyl](2-phenoxyethyl)amine, a potent α1and 5-HT1Areceptor ligand: an in vitro and computational study

Enantioseparation and stereoselective synthesis of (1), a potent 5-HT_1AR ligand, were performed. Surprisingly, the enantiomers proved to be equipotent.

Classification of 5-HT(1A) receptor agonists and antagonists using GA-SVM method

AIM

To construct a reliable computational model for the classification of agonists and antagonists of 5-HT(1A) receptor.

METHODS

Support vector machine (SVM), a well-known machine learning method, was employed to build a prediction model, and genetic algorithm (GA) was used to select the most relevant descriptors and to optimize two important parameters, C and r of the SVM model. The overall dataset used in this study comprised 284 ligands of the 5-HT(1A) receptor with diverse structures reported in the literatures.

RESULTS

A SVM model was successfully developed that could be used to predict the probability of a ligand being an agonist or antagonist of the 5-HT(1A) receptor. The predictive accuracy for training and test sets was 0.942 and 0.865, respectively. For compounds with probability estimate higher than 0.7, the predictive accuracy of the model for training and test sets was 0.954 and 0.927, respectively. To further validate our model, the receiver operating characteristic (ROC) curve was plotted, and the Area-Under-the-ROC- Curve (AUC) value was calculated to be 0.883 for training set and 0.906 for test set.

CONCLUSION

A reliable SVM model was successfully developed that could effectively distinguish agonists and antagonists among the ligands of the 5-HT(1A) receptor. To our knowledge, this is the first effort for the classification of 5-HT(1A) receptor agonists and antagonists based on a diverse dataset. This method may be used to classify the ligands of other members of the GPCR family.

Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) studies on α(1A)-adrenergic receptor antagonists based on pharmacophore molecular alignment

The α_1A-adrenergic receptor (α_1A-AR) antagonist is useful in treating benign prostatic hyperplasia, lower urinary tract symptoms, and cardiac arrhythmia. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed on a set of α_1A-AR antagonists of N-aryl and N-nitrogen class. Statistically significant models constructed from comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were established based on a training set of 32 ligands using pharmacophore-based molecular alignment. The leave-oneout cross-validation correlation coefficients were q²_CoMFA = 0.840 and q²_CoMSIA = 0.840. The high correlation between the cross-validated/predicted and experimental activities of a test set of 12 ligands revealed that the CoMFA and CoMSIA models were robust (r²_pred/CoMFA = 0.694; r²_pred/CoMSIA = 0.671). The generated models suggested that electrostatic, hydrophobic, and hydrogen bonding interactions play important roles between ligands and receptors in the active site. Our study serves as a guide for further experimental investigations on the synthesis of new compounds. Structural modifications based on the present 3D-QSAR results may lead to the discovery of other α_1A-AR antagonists.